A variety of technologies can be used to investigate biological processes and anatomy. The following examples are types of scan that may be used to provide medical images: X-Ray; Computed Tomography (CT); Ultrasound (US); Magnetic Resonance Imaging (MRI); Single Photon Emission Tomography (SPECT); and Positron Emission Tomography (PET). Each type of scan is referred to as an ‘imaging modality’.
In medical imaging, typically, digital 3-dimensional images are produced. Medical imaging workstations are commonly used to allow the viewing and manipulation of these images. Medical images are read, analysed and reviewed by specialists, for example radiologists.
Typically, a scan provides a ‘dataset’. The dataset comprises digital information about the value of a variable at each of many points. The points are different spatial locations that are spread throughout 3 physical dimensions, i.e. each point is at a particular location on a three dimensional grid. The variable may typically be an intensity measurement. The intensity may be, for example, an indication of the X-Ray attenuation of the tissue at each particular point.
In such a three dimensional dataset, the element of the scan image located at a particular spatial location may be referred to as a ‘voxel’. A voxel is therefore analogous to a ‘pixel’ of a conventional 2-Dimensional image.
Although the dataset of the medical scan is 3-Dimensional, it is typically displayed as a two dimensional image to a user on a medical imaging workstation. The 3-D scan may be divided up into tens or hundreds of parallel, 2-D images. The user of a workstation can then flick through the images in sequence, for example, thereby allowing a view of successive cross sections of the tissue that was scanned.
Typical workstations allow the 2-D slices to be viewed individually or sequentially in successive steps along a selected one of three perpendicular directions. For a human subject lying down, the axes of the three perpendicular directions may, for example, be along the ‘long axis’ of the body, ‘across’ the body from one side to the other, and through the body from top to bottom. These axes are conventionally referred to as: ‘axial’ (for cross-sections that lie along the long axis of the body), coronal (for cross-sections that lie along an axis running from the front to back) and sagittal (cross-sections that lie along an axis that runs from side to side.)
Henceforth, the term ‘scan image’ should be construed as meaning a three dimensional dataset that results from performing a medical scan. However, when the scan image is displayed, only a two dimensional slice of the dataset may be on view at any one time. Medical images usually have as their subject humans. However, images may also be obtained of non-human animals, particularly as part of medical research projects.
Medical images may include information about a wide variety of anatomical features and structures. For example, an image may show various types of healthy tissue, such as bone and organs within the body. An image may also show abnormal tissues. The term ‘lesion’ is often used to describe various types of abnormal tissue. One common example of a lesion is a tumour. However, an image may also show other types of lesions, such as cysts or swollen glands. The word ‘lesion’ should henceforth be construed to include both tumours and other types of abnormal tissues.
The purpose of obtaining a medical image is often to detect abnormal tissue. So, a typical example of an application of medical imaging is in the identification and ‘staging’ of cancerous tumours. Images may also be required for assessing the efficacy of any administered treatment. Images from scans may however not lead unambiguously to an identification of whether or not a given part of the scan shows abnormal tissue. The identification of the pathology of tissue observed in a scan is still not possible with 100% certainty. As a consequence, for example, patients may subsequently need to be subjected to biopsies, in order to obtain a sample of tissues whose nature is not clear on a scan.
‘Multiple modalities’ may be used to provide medical images. This approach involves obtaining images of the same region of tissue by more than one modality. For example, the same region of tissue may be imaged using both a PET scan and a CT scan. Another important example of a multiple mode scan is a SPECT/CT scan. Both PET/CT and SPECT/CT scans combine the predominantly anatomical and structural information obtained from a CT scan with a scan which measures the biological function.
Scanners that can carry out multiple mode scans are referred to as ‘hybrid scanners’. Typically, a hybrid scanner allows the subject to be scanned by both modalities in the same sitting.
A multimodal scan such as a PET/CT or SPECT/CT scan may provide the following advantages:    1) It provides the reader with both anatomical and structural information, as well as functional information.    2) The CT scan can be used to correct for the attenuation to which the SPECT or PET signal is subjected, as it travels through the body.    3) It allows the reader to localise areas of PET or SPECT signal to particular regions of the body.    4) The patient benefits from fewer examinations.    5) A given scanning machine will provide more information for any given number of patients who are scanned, than a single mode scanner, thereby delivering enhanced ‘utilisation’ for a hospital.
The precise biological signal measured by a PET or SPECT scan is dependent on the radiotracer used. For PET, the most widespread radiotracer is fluorodeoxyglucose (FDG), which is a sugar. FDG acts as a surrogate for glucose, thereby facilitating the measurement of metabolic activity within a region of tissue.
Malignant tumours are highly metabolically active. They therefore tend to appear as bright areas in a PET image. Such active tissues are often referred to as being ‘FDG avid’.
A problem may arise due to the fact that there are many normal physiological processes that are also FDG avid. For example, the brain also consumes large amounts of energy. So the brain appears as a bright region on a PET image. The same applies to the heart, the bladder and parts of the kidneys.
Normally, such well localised anatomical regions can be identified by experienced personnel. This identification usually just requires examination of the PET image alone, and rarely requires examination of the corresponding CT image of any multimodal scan, to allow these regions to be categorically ruled out in any search for abnormal tissue. However, some lesions that are near the edges of FDG avid organs may be less clearly differentiated. In these cases, the normal way to decide the likely cause of a bright region in the PET image is by looking at the CT scan.
Other, more subtle sources of apparently FDG avid regions may be seen in images. Examples include:    (i) The larynx, which may exhibit some FDG activity if the subject spoke after injection of the radiotracer.    (ii) Artificial implants. In a PET/CT multimodal scan, the CT can be used to correct for the photon attenuation observed when the photons travel through different parts of the body. This correction process relies on a correct estimation of the attenuation that a photon undergoes. However, the presence of implants and mis-alignment of the two scans may cause errors in the estimation. The errors lead to ‘artefacts’ in the PET post attenuation correction.    (iii) Inflammation processes.    (iv) Metabolically active brown-fat.
These sources (i)-(iv) are more problematic than areas such as the brain, and may lead to ‘false positive’ diagnoses. A ‘false positive’ is a case where a normal area of tissue is mistakenly classified as abnormal.
In summary, a key problem in the interpretation of medical image scans is the determination of the underlying cause of apparent activity. This problem arises particularly when considering radiotracer activity in a PET or SPECT image.
Three more detailed prior art systems are listed under points 1-3 below. The ‘Computer Aided Detection/Diagnosis systems’ and ‘Basic histogram’ approach are essentially techniques for interpreting information obtained in scans. The ‘MR spectroscopy’ approach is a different type of scan, which provides different data to those described above.
1. Computer Aided Detection/Diagnosis Systems
Many computerised approaches have been proposed to aid the clinician in detecting and diagnosing disease. So-called ‘Computer Aided Detection and Diagnosis’ systems aim to do this by indicating to the user locations in the image that are likely to correspond to disease. However, such systems are designed to indicate the presence or absence of disease. They operate by examining patterns in the image, and categorising them as ‘disease’ or ‘non-disease’. Such systems have been proposed mostly for single image, single modality applications like X-ray mammography, lung CT and colon CT. Furthermore, such systems do not integrate well into the clinical work-flow, because they tend only to show limited information about the images.
2. Basic Histogram
Medical image workstations may include functionality that enables them to display a histogram of the distribution of intensities within a user defined region of interest in a scan. Such histograms are typically ‘binned’ according to some uniform bin width, for example every 100 units of intensity. Alternatively, they may be specified to have a certain number of bins. Information may be provided to the user on e.g. the mean of the values in a region of interest, or the maximum.
3. Spectroscopy
MR spectroscopy is a particular type of MR scan, in which the chemical composition of regions of tissue in a patient can be identified in vivo. The method relies on the different nuclear magnetic resonance signal of different chemical compounds. The result is often displayed to the user as a graphical plot, which shows the proportion of different compounds. MR spectroscopy leads directly to an identification of the chemical composition of a region of tissue.